Fast and accurate head pose estimation via random projection forests

Donghoon Lee; Ming Hsuan Yang; Songhwai Oh

doi:10.1109/ICCV.2015.227

Fast and accurate head pose estimation via random projection forests

Donghoon Lee, Ming Hsuan Yang, Songhwai Oh

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

37 Citations (Scopus)

Abstract

In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image. Under this condition, reliably extracting facial features is very difficult. We propose a novel head pose estimation algorithm based on compressive sensing. Head image patches are mapped to a large feature space using the proposed extensive, yet efficient filter bank. The filter bank is designed to generate sparse responses of color and gradient information, which can be compressed using random projection, and classified by a random forest. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods on head pose estimation in low-resolution images degraded by noise, occlusion, and blurring.

Original language	English
Title of host publication	2015 International Conference on Computer Vision, ICCV 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1958-1966
Number of pages	9
ISBN (Electronic)	9781467383912
DOIs	https://doi.org/10.1109/ICCV.2015.227
Publication status	Published - 2015 Feb 17
Event	15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile Duration: 2015 Dec 11 → 2015 Dec 18

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)	1550-5499

Other

Other	15th IEEE International Conference on Computer Vision, ICCV 2015
Country/Territory	Chile
City	Santiago
Period	15/12/11 → 15/12/18

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

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10.1109/ICCV.2015.227

Cite this

Lee, D., Yang, M. H., & Oh, S. (2015). Fast and accurate head pose estimation via random projection forests. In 2015 International Conference on Computer Vision, ICCV 2015 (pp. 1958-1966). Article 7410584 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2015.227

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title = "Fast and accurate head pose estimation via random projection forests",

abstract = "In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image. Under this condition, reliably extracting facial features is very difficult. We propose a novel head pose estimation algorithm based on compressive sensing. Head image patches are mapped to a large feature space using the proposed extensive, yet efficient filter bank. The filter bank is designed to generate sparse responses of color and gradient information, which can be compressed using random projection, and classified by a random forest. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods on head pose estimation in low-resolution images degraded by noise, occlusion, and blurring.",

author = "Donghoon Lee and Yang, {Ming Hsuan} and Songhwai Oh",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Computer Vision, ICCV 2015 ; Conference date: 11-12-2015 Through 18-12-2015",

year = "2015",

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doi = "10.1109/ICCV.2015.227",

language = "English",

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Lee, D, Yang, MH & Oh, S 2015, Fast and accurate head pose estimation via random projection forests. in 2015 International Conference on Computer Vision, ICCV 2015., 7410584, Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 International Conference on Computer Vision, ICCV 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1958-1966, 15th IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 15/12/11. https://doi.org/10.1109/ICCV.2015.227

Fast and accurate head pose estimation via random projection forests. / Lee, Donghoon; Yang, Ming Hsuan; Oh, Songhwai.
2015 International Conference on Computer Vision, ICCV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1958-1966 7410584 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image. Under this condition, reliably extracting facial features is very difficult. We propose a novel head pose estimation algorithm based on compressive sensing. Head image patches are mapped to a large feature space using the proposed extensive, yet efficient filter bank. The filter bank is designed to generate sparse responses of color and gradient information, which can be compressed using random projection, and classified by a random forest. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods on head pose estimation in low-resolution images degraded by noise, occlusion, and blurring.

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Fast and accurate head pose estimation via random projection forests

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